[Retracted] Prediction and Application of Computer Simulation in Time‐Lagged Financial Risk Systems

Abstract

Based on the existing financial system risk models, a set of time-lag financial system risk models is established considering the influence brought by time-lag factors on the financial risk system, and the dynamical behavior of this system is analyzed by using chaos theory. Through Matlab simulation, the bifurcation diagram and phase diagram of time-lag risk intensity and control intensity are plotted. The analysis shows that this kind of time-lag financial system risk model has complex dynamic behavior, different motion states will appear when different parameter values are selected, and the time-lag risk intensity parameter also has a very strong influence on the system motion. To ensure the operation of the financial system in a stable state, measures with certain delay effects must be taken to control the risk and to choose the appropriate time-lag control intensity, and too much or too little time-lag control intensity is not conducive to the benign operation of the system.

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

“…Wu [16] has obtained more accurate results by combining an Auto Regressive Integrated Moving Average (ARIMA) model with a Back Propagation Neural Network (BPNN) for predictive analysis of future stress indices. Building on existing financial system risk models and taking into account the effects of time lags employed in the financial risk system, Wang et al [17] have established a set of time-delayed financial system risk models through the analysis of the system's dynamic behavior using chaos theory. Tiwari et al [18] analyzed the monthly data of the extracted financial stress index and the uncertainty index related to national policies, etc., and came up with the result that financial stress plays an important role in economic activities.…”

A deep learning model-based approach to financial risk assessment and prediction

“…For the assessment and prediction of financial risk, the literature [12] used Matlab simulation to draw a bifurcation diagram of the intensity of time-lagged risk and built a time-lagged risk financial system model based on it. The literature [13] constructed a system for extracting financial information based on the seq2seq model and then classified the extracted useful information by the GRU model.…”

Retracted: Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”